3D Colored Shape Reconstruction from a Single RGB Image through Diffusion

TL;DR

This paper introduces a novel diffusion-based method for reconstructing 3D colored shapes from a single RGB image, integrating shape, color, and rendering modules for high-quality results.

Contribution

It is the first to apply a diffusion model for 3D colored shape reconstruction from a single image, combining shape prediction, color prediction, and NeRF-like rendering.

Findings

Achieves competitive 3D colored shape reconstruction performance.

The color prediction module enhances the quality of 3D geometric reconstruction.

Demonstrates the effectiveness of diffusion models in 3D shape and color generation.

Abstract

We propose a novel 3d colored shape reconstruction method from a single RGB image through diffusion model. Diffusion models have shown great development potentials for high-quality 3D shape generation. However, most existing work based on diffusion models only focus on geometric shape generation, they cannot either accomplish 3D reconstruction from a single image, or produce 3D geometric shape with color information. In this work, we propose to reconstruct a 3D colored shape from a single RGB image through a novel conditional diffusion model. The reverse process of the proposed diffusion model is consisted of three modules, shape prediction module, color prediction module and NeRF-like rendering module. In shape prediction module, the reference RGB image is first encoded into a high-level shape feature and then the shape feature is utilized as a condition to predict the reverse geometric noise in diffusion model. Then the color of each 3D point updated in shape prediction module is predicted by color prediction module. Finally, a NeRF-like rendering module is designed to render the colored point cloud predicted by the former two modules to 2D image space to guide the training conditioned only on a reference image. As far as the authors know, the proposed method is the first diffusion model for 3D colored shape reconstruction from a single RGB image. Experimental results demonstrate that the proposed method achieves competitive performance on colored 3D shape reconstruction, and the ablation study validates the positive role of the color prediction module in improving the reconstruction quality of 3D geometric point cloud.